Efficient string search

ABSTRACT

Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

BACKGROUND OF THE INVENTION

The present application is a continuation and claims the prioritybenefit of U.S. patent application Ser. No. 14/326,230 filed Jul. 8,2014, now U.S. Pat. No. 9,542,387, which is a continuation and claimsthe priority benefit of U.S. patent application Ser. No. 13/973,859filed Aug. 22, 2013, now U.S. Pat. No. 8,775,164, which is acontinuation and claims the priority benefit of U.S. patent applicationSer. No. 13/335,743 filed Dec. 22, 2011, now U.S. Pat. No. 8,577,669,which is a continuation and claims the priority benefit of U.S. patentapplication Ser. No. 11/881,556 filed Jul. 27, 2007, now U.S. Pat. No.8,086,441, the disclosures of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION

Field of the Invention

Embodiments of the present invention relate to classifying content, andmore specifically to searching for one or more predetermined N-grams ina string of bytes representing content written in a non-delimitedlanguage.

Description of the Related Art

Today, many entities (e.g., private companies, government, schools,etc.) rely on various content filtering mechanisms to manage and/orcontrol user access to the Internet via facilities provided by theentities. For example, a company typically implements some form ofcontent filtering mechanism to control the use of the company'scomputers and/or servers to access contents (e.g., web pages and/oremails) from the Internet. Contents as used herein broadly refer toexpressive work, which may include one or more of literary, graphics,audio, and video data. Access to content within certain predeterminedcategories using the company's computers and/or servers may not beallowed during some predetermined periods of time.

Conventionally, a content rating engine or a content classificationengine may be installed in a firewall to screen contents coming into asystem from an external network, such as email received and web pagesretrieved from the Internet. The content rating engine may retrieverating of the incoming contents from a rating database, if any, and/orattempt to rate the contents in real-time. To rate the content inreal-time, the content rating engine may parse the contents to identifysome predetermined keywords and/or tokens and then determine a ratingfor the contents based on the presence and/or absence of the keywordsand/or tokens.

For European languages (e.g., English, French, etc.), the spaces betweenwords are often used as delimiters for recognizing word boundaries.Therefore, words in European languages can be readily tokenized andsearched using the spaces between the words. As a result, tokenizationgenerally proceeds efficiently for European languages.

However, the above approach typically fails for languages that lackspaces between words, such as Chinese, Japanese, Thai, etc. Suchlanguages are also referred to as non-delimited languages herein. Forexample, a Chinese sentence is composed of words, which contain avariable number of characters, with no spaces indicating the wordboundaries. Below is an example of an excerpt from a Chinese newspaper:“

,

” The words are

(now)

(one)

(week)

(ago)

(Iranian)

(government)

(began)

(implementing)

(energy)

(rationing) . . . . Note that the characters are not separated byspaces, and a word may include one or more characters. Other examplescan be more complicated, with ambiguous sentences where the correctsplit of text into words can be found only by understanding the context.As a result, spaces may not be reliably used as delimiters forrecognizing words in Chinese. Because of the above issue, keyword searchin non-delimited languages is typically difficult and time consuming.This is particularly problematic in real-time or on-the-fly contentfiltering because the keyword search has to be limited to avoid causinga noticeable delay in online content access.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in which:

FIG. 1A illustrates one embodiment of a process to construct a finitestate machine to search for a set of N-grams in a string of bytes.

FIG. 1B illustrates one embodiment of a process to search for a set ofN-grams in a string of bytes.

FIG. 1C illustrates an exemplary embodiment of a finite state machine.

FIG. 2A illustrates one embodiment of a process to generate a model forclassifying content.

FIG. 2B illustrates one embodiment of a process to classify content.

FIG. 3A illustrates a functional block diagram of one embodiment of asystem to generate models for classifying content.

FIG. 3B illustrates a functional block diagram of one embodiment of asystem to classify content.

FIG. 4 illustrates a block diagram of an exemplary computer system.

DETAILED DESCRIPTION

Described herein are some embodiments of an efficient string search. Asmentioned above, content as used herein broadly refers to expressivework, which may include one or more of literary, graphics, audio, andvideo data. Online content generally refers to content accessible over anetwork (e.g., an intranet, the Internet, etc.). Some examples of onlinecontent include web pages, electronic mails, etc. Furthermore, contentmay include text in various formats and/or languages. In general,languages may be categorized as delimited languages and non-delimitedlanguages in the current document. Delimited languages refer tolanguages composed of words that are separated by spaces, where there isno space within a single word. On the contrary, non-delimited languagesrefer to languages composed of words, where a word may include zero ormore spaces within itself. For example, Chinese is a non-delimitedlanguage, where a Chinese sentence is composed of words, which contain avariable number of characters, with no spaces indicating the wordboundaries. In order to efficiently search for keywords in anon-delimited language, a set of N-grams representing the keywords areused instead of word tokens. Generally speaking, an N-gram is a sequenceof N items, where N is an integer. Each N-gram corresponds to a keywordpre-selected for identifying content of a certain type in a particularnon-delimited language.

In some embodiments, a string of bytes representing content written in anon-delimited language is received, wherein the content has beenclassified into a predetermined category. In a single pass through thestring of bytes, a set of N-grams is searched for substantiallysimultaneously. Statistical information on occurrences of the N-grams,if any, in the string of bytes is collected. The efficient string searchdisclosed herein may be used in various applications, such as generationof models for document classification, classification of documentsduring screening of online content, etc. More details of someembodiments of the efficient string search are described below.

In the following description, numerous details are set forth. It will beapparent, however, to one skilled in the art, that the present inventionmay be practiced without these specific details. In some instances,well-known structures and devices are shown in block diagram form,rather than in detail, in order to avoid obscuring the presentinvention.

Some portions of the detailed descriptions below are presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission, or display devices.

The present invention also relates to apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in amachine-readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus. Such a medium may also be referred to as amachine-accessible medium.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required operations. The required structure fora variety of these systems will appear from the description below. Inaddition, the present invention is not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the teachings of theinvention as described herein.

FIG. 1A illustrates one embodiment of a process to construct a finitestate machine to search for a set of N-grams in a string of bytes. Theprocess may be performed by processing logic that may comprise hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), firmware, ora combination thereof. For example, the server 310 in FIG. 3A mayperform at least part of the process in some embodiments.

Referring to FIG. 1A, processing logic receives a set of N-grams(processing block 110). This set of N-grams may also be referred to as afeature set. As mentioned above, an N-gram is a sequence of N items,where N is an integer. Each N-gram corresponds to a keyword pre-selectedfor identifying content of a certain type in a particular non-delimitedlanguage. For instance, a feature set for identifying pornographiccontent may include keywords that are frequently used in pornographiccontent. Since the keywords are words in a non-delimited language, theremay be zero or more spaces between one or more characters within eachkeyword. Furthermore, the keywords may include different number ofcharacters. As such, the N-grams within the feature set may be ofdifferent lengths (e.g., one byte, two bytes, three bytes, etc.). Notethat for large character sets, such as a character set in Chinese, onecharacter is not always one byte. Some popular encodings (e.g., UnicodeTransformation Format-8 (UTF-8)) use a variable number of bytes percharacter. However, the approach disclosed herein handles such scenariosmoothly because the approach makes no assumption on the number of bytesin a pattern in the feature set.

Based on the feature set of N-grams, processing logic defines a set ofstates (processing block 112). In some embodiments, there is one statefor every string that can be a prefix of one or more terms in thekeyword list. For example, if the feature set of N-grams include “aab”,“abb” and “aaabb,” where “a” and “b” represent two characters usable tocompose different words in a non-delimited language, then there may bestates corresponding to “a”, “aa”, “aab”, “ab”, “abb”, “aaa”, “aaab”,and “aaabb” (all possible prefixes of each N-gram in the feature set).Note that a single prefix may pertain to more than one keyword, and alsonote that whole keywords may be considered to be prefixes of themselves.The set of states may be defined by a simple loop over each keywordexamining every prefix. In addition, there may be a “start” and an “end”states defined.

Processing logic then constructs a finite state machine (FSM) from theset of states defined (processing block 114). To construct the FSM,processing logic may connect the states to each other as follows. Forevery state, processing logic reaches the following state by adding aninput symbol corresponding to a new prefix, or returns to the start ofany new prefix. Continuing with the example above, for a statecorresponding to “aa” and an input symbol “a,” the next state is the onecorresponding to “aaa.” If the input symbol is “b” the next state is“aab”, or if the input symbol is “c,” processing logic returns to the“start” state (since “aac” is not a prefix of any keyword in the featureset). If the end of an input string is reached, processing logic returnsto the “end” state of the FSM. In some embodiments, processing logicfurther construct a transition table for the FSM. To construct thetransition table, processing logic may step through every state andevery possible input symbol and determine whether the new string is aprefix with a corresponding state. If so, processing logic maytransition from the old state to the new state over this input. If not,processing logic may add a transition back to the “start” state. Oneexample of a FSM 130 constructed based on the above exemplary featureset of {“aab”, “abb”, “aaabb”} is shown in FIG. 1C. Note that the FSM130 transitions back to the “start” state 1301 from every state in theFSM 130 if the next input is an input other than “a” or “b” (except the“end” state 1309). However, to avoid obscuring the view in FIG. 1C,these transitions back to the “start” state 1301 are not shown.

Referring back to FIG. 1A, processing logic maps each state to a set ofzero or more N-grams of the feature set after constructing the FSM(processing block 116). Processing logic may store mappings of thestates to the N-grams in an output table. To map the states to theN-grams, processing logic may use the following approach. If the statecorresponds to the end of a complete keyword, processing logic mayreturn this keyword. For example, the state “aaab” finishes the completekeyword “aab,” so the complete keyword “aab” is outputted. The state“aaabb” finishes the keywords “abb” and “aaabb,” so these two keywordsare outputted. If a state does not finish any complete keyword, null maybe outputted. To construct the output table, processing logic mayexamine each state and determine whether the corresponding prefix stringof the state completes any keywords. If so, processing logic may addthese keywords to the output of the state. One example of an outputtable for the FSM 130 in FIG. 1C is shown below:

TABLE 1 An exemplary output table State N-gram(s) a Null aa Null ab Nullaaa Null aab aab aaab aab abb abb aaabb abb, aaabb

FIG. 1B illustrates one embodiment of a process to search for the set ofN-grams in a string of bytes using the FSM described above. The processmay be performed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), firmware, ora combination thereof. For example, the content filter 320 in FIG. 3Bmay perform at least part of the process in some embodiments.

Referring to FIG. 1B, processing logic inputs a string of bytes into theFSM constructed (processing block 120). The string of bytes representscontent written in a non-delimited language, which may be part of a filebeing downloaded, an electronic mail, a web page, etc. For example, thestring of bytes may be at least part of the text on a Japanese web pagethat a user attempts to access. Then processing logic traces theoperations of the FSM over the string of bytes (processing block 122).While tracing the operations of the FSM, processing logic collectsstatistical information (processing block 124). For example, thestatistics may include the number of occurrences of the N-grams, if any,in the string of bytes. In some embodiments, the mappings of the statesof the FSM to the corresponding N-grams are stored in an output table,such as Table 1 above. As the FSM transitions into a state that ismapped to one or more N-grams, the FSM outputs the one or more N-grams.Processing logic may keep track of the numbers of occurrences of theseN-grams in the string of bytes as the FSM outputs the N-grams.

At processing block 126, processing logic checks whether the end of thestring of bytes has been reached. If not, processing logic returns toprocessing block 122 to continue searching through the string of bytes.Otherwise, the process ends at processing block 128.

The above approach to search for N-grams in the string of bytes is morereliable and efficient than many conventional approaches fornon-delimited languages because the above approach does not depend onany delimiters between words. As mentioned above, a non-delimitedlanguage may not provide any delimiters between words. Moreover, theabove approach simultaneously searches for the N-grams in a single passthrough the string of bytes. Thus, this approach remains efficient evenfor long strings. Such efficient string searching technique has manypractical applications, two of which are discussed in details below toillustrate the concept. The first application is model generation andthe second application is content classification.

FIG. 2A illustrates one embodiment of a process to generate a model forclassifying content. The process may be performed by processing logicthat may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (such as instructions runon a processing device), firmware, or a combination thereof. Forexample, the server 310 in FIG. 3A may perform at least part of theprocess in some embodiments.

In some embodiments, processing logic performs an efficient stringsearch on a pre-classified document to search for a set of N-grams inthe document (processing block 210). Some embodiments of the efficientstring search have been described above. The pre-classified document mayinclude a web page, an electronic mail message, etc. The content of thepre-classified document has been classified into a certain category(e.g., pornographic content, violent content, etc.). After performingthe efficient string search on the document, processing logic generatesa model based on the statistical information on the occurrences of theN-grams in the pre-classified document (processing block 212). The modelmay be made available to content filters (processing block 214). Usingthe model, the content filters may classify the content of an incomingstring of bytes, and then, may determine if access to the content isallowed under some predetermined policies.

FIG. 2B illustrates one embodiment of a process to classify content. Theprocess may be performed by processing logic that may comprise hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), firmware, ora combination thereof. For example, the content filter 320 in FIG. 3Bmay perform at least part of the process in some embodiments.

Referring to FIG. 2B, processing logic receives an incoming string ofbytes (processing block 220). The string of bytes may be part of a webpage requested by a client, an electronic mail message directed to theclient, an article being downloaded from a network in response to arequest from the client, etc. Furthermore, the string of bytesrepresents content written in a non-delimited language. Then processinglogic performs an efficient string search on the string of bytes(processing block 222). Some embodiments of the efficient string searchhave been described above.

While performing the search on the string, statistical information onthe occurrences of a set of predetermined N-grams (e.g., the feature setof N-grams discussed above) in the string is collected. Using thestatistical information collected and a model (such as the modelgenerated according to FIG. 2A), processing logic classifies the contentof the string of bytes (processing block 224). For example, the modelmay be a model for identifying pornographic content, thus, the model mayalso be referred to as a pornographic model. The pornographic model mayinclude one or more conditions that have to be satisfied before a stringof bytes is classified as pornographic, such as a first N-gram has tooccur more than a certain number of times for a string of a certainlength, etc. If a predetermined number of conditions in the pornographicmodel have been satisfied by the statistics of the string, thenprocessing logic may classify the content of the string to bepornographic.

Based on the classification and some predetermined policies, processinglogic determines if a user should be allowed to access the string ofbytes (processing block 226). For instance, a school may have a policybarring access of pornographic material using school computers. Thus,processing logic may determine to deny access to the string by a user atthe school if the content is classified as pornographic.

In some embodiments, processing logic causes a client machine to renderthe string of bytes if the user is allowed to access the content(processing block 228). For example, processing logic may forward thestring of bytes to the client machine, which may execute a networkaccess application (e.g., an Internet browser, an email engine, adocument viewing application, etc.) to display the content. Otherwise,if the user is not allowed to access the content, processing logicblocks the string and causes the client machine to render an errormessage (processing block 229). For example, processing logic may sendan error signal to the client machine, which may generate the errormessage and display the error message via the network access applicationand/or in a pop-up window.

FIG. 3A illustrates a functional block diagram of one embodiment of asystem to generate models for classifying content. The system 300Aincludes a server 310 and a model repository 318. The server 310 furtherincludes a finite state machine (FSM) 312, a counting module 314, and amodel generator 316. The server 310 may be implemented using a computingmachine, such as the one illustrated in FIG. 4. To illustrate theoperations of the system 300A, one example is discussed in detailsbelow.

In some embodiments, the server 310 receives a set of predeterminedN-grams. The N-grams may include bytes representing various keywords ina non-delimited language. Note that the N-grams may be of differentlengths. The keywords may be chosen based on their likelihood ofoccurrences in a particular type of content. For example, keywords, suchas “kill,” “blood,” “gun,” etc., are more likely to appear in violentcontent, and thus, these keywords may be included in the set of N-grams.Based on the set of N-grams, the server 310 defines a set of states andconstructs the FSM 312. Details of some embodiments of the definition ofstates based on the N-grams and the construction of the FSM 312 havebeen described above.

The server 310 further receives a string of bytes 301 representing apre-selected document (e.g., a web page, an electronic mail message,etc.) in the non-delimited language. The document has been classifiedinto a particular category (e.g., violent content, pornographic content,etc.). The string of bytes 301 is input to the FSM 312 within the server310, which performs an efficient string search on the string of bytes301 to search for the set of N-grams, if any. The FSM 312 simultaneouslysearches for the set of N-grams through the string of bytes 301 in asingle pass. As the FSM 312 goes through the string of bytes 301, theFSM 312 may output the matching N-gram(s) found in the string of bytes301. Details of some embodiments of the efficient string searchperformed by the FSM 312 have been discussed above.

The output from the FSM 312 is provided to the counting module 314,which counts the number of occurrences of each of the N-grams in thestring of bytes 301. Then the counting module 314 sends the numbers ofoccurrences of the N-grams to the model generator 316. The modelgenerator 316 uses the numbers of occurrences of the N-grams to generatea model for classifying content. The model may be stored in the modelrepository 318, which may be accessible by content filter clients acrossa network (e.g., an intranet, the Internet, etc.). Alternatively, themodel may be transferred or downloaded to content filter clients, whichstore the model in storage devices (e.g., ROM, RAM, etc.) within thecontent filter clients. More details on classifying content using themodel are discussed below.

FIG. 3B illustrates a functional block diagram of one embodiment of asystem to classify content. The system 300B includes a content filteringclient 320, a client machine 330, a model repository 333, and a network340. The network 340 and the model repository 318 are coupled to thecontent filtering client 320. The content filtering client 320 isfurther coupled to the client machine 330. In some embodiments, thecontent filtering client 320 acts as a firewall between the clientmachine 330 and the network 340. Alternatively, the content filteringclient 320 acts as a spam filter for the client machine 330 to screenout electronic mail messages classified as spam. The client machine 330may include a computing machine, such as a desktop personal computer(PC), a laptop PC, a personal digital assistant (PDA), a mobiletelephone, the computing machine illustrated in FIG. 4, etc. A networkaccess application (e.g., a browser, an electronic mail engine, etc.)may be executed on the client machine 330. The network 340 may includeone or more kinds of network, such as an intranet, the Internet, etc. Inthe current example, the model repository 333 is directly coupled to thecontent filtering client 320. Alternatively, the model repository 333may be indirectly coupled to the content filtering client 320 via thenetwork 340.

In some embodiments, the content filtering client 320 may be implementedin a set-top box having components, such as, for example, a processor,network interface, one or more storage devices (e.g., RAM, ROM, flashmemory, etc.), etc. Alternatively, the content filtering client 320 maybe implemented on a proxy server (also referred to as a gateway server).A functional block diagram of the content filtering client 320 isillustrated in FIG. 3B.

Referring to FIG. 3B, the content filtering client 320 includes a FSM322, a counting module 324, a classifying engine 326, and a contentfilter 328. The content filtering client 320 receives a string of bytes331 from the network 340. The string of bytes 331 is forwarded to boththe content filter 328 and the FSM 322. The FSM 322 may include statesdefined based on a set of N-grams as discussed above. The string ofbytes 331 may represent content written in a non-delimited language(e.g., a web page, an electronic mail message, etc.). Then the FSM 322performs an efficient string search on the string of bytes 331 to lookfor the N-grams. Details of some embodiments of the efficient stringsearch have been discussed above. As the FSM 322 searches through thestring of bytes 331, the FSM 322 may output N-gram(s) found in thestring of bytes 331. Based on the output from the FSM 322, the countingmodule 324 collects statistics on the occurrences of the N-grams in thestring of bytes 331. For instance, the counting module 324 may count thenumbers of occurrences of the N-grams in the string of bytes 331. Thecounting module 324 then provides the numbers of occurrences of theN-grams to the classifying engine 326. The classifying engine 326further receives a model from the model repository 333.

In some embodiments, the classifying engine 326 compares the statisticalinformation collected against the model in order to classify the contentrepresented by the string of bytes 331. Details of some embodiments ofcontent classification have been discussed above. After classifying thecontent, the classifying engine 326 notifies the content filter 328 ofthe classification of the content. The content filter 328 then decideswhether to allow the string 331 to pass through or to block the string331 based on the classification.

For example, the client machine 330 may be a laptop computer provided bya company to its employee, and thus, the client machine 330 is notallowed to access pornographic materials under company policy. If theclassifying engine 326 classifies the string of bytes 331 to bepornographic, then the content filter 328 may block the string of bytes331 from the client machine 330. Further, the content filter 328 maytake additional courses of action, such as generating an error messageto inform the user of the client machine 330 that access to the contentrepresented by the string of bytes 331 is denied, reporting theattempted access to a system administrator, recording information of theattempted access (e.g., time of access, user logged into the clientmachine 330, source of the string 331, etc.).

On the other hand, if the classifying engine 326 classifies the stringof bytes 331 to be non-pornographic, the content filter 328 may allowaccess to the content and forward the string 331 to the client machine330.

In another example, the classifying engine 326 classifies the string ofbytes 331 to be spam and the user has previously requested to block allspam. Then the content filter 328 may block the string of bytes 331 fromthe client machine 330.

In some embodiments, the client machine 330 may render the contentrepresented by the string of bytes 331 if the string of bytes 331 isforwarded from the content filtering client 320. For example, the clientmachine 330 may include a display device and an application (e.g., abrowser, a document viewing application, etc.) executable on the clientmachine 330 may render the content via the display device. Otherwise,the client machine 330 may render the error message from the contentfilter 328 if access to the string 331 is denied because of theclassification of the content.

FIG. 4 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 400 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, and/or the Internet. Themachine may operate in the capacity of a server or a client machine inclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, a switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The exemplary computer system 400 includes a processing device 402, amain memory 404 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 406 (e.g., flash memory, staticrandom access memory (SRAM), etc.), and a data storage device 418, whichcommunicate with each other via a bus 430.

Processing device 402 represents one or more general-purpose processingdevices such as a microprocessor, a central processing unit, or thelike. More particularly, the processing device may be complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 402 may also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 402 is configuredto execute the processing logic 426 for performing the operations andsteps discussed herein.

The computer system 400 may further include a network interface device408. The computer system 400 also may include a video display unit 410(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), analphanumeric input device 412 (e.g., a keyboard), a cursor controldevice 414 (e.g., a mouse), and a signal generation device 416 (e.g., aspeaker).

The data storage device 418 may include a machine-accessible storagemedium 430 (also known as a machine-readable storage medium) on which isstored one or more sets of instructions (e.g., software 422) embodyingany one or more of the methodologies or functions described herein. Thesoftware 422 may also reside, completely or at least partially, withinthe main memory 404 and/or within the processing device 402 duringexecution thereof by the computer system 400, the main memory 404 andthe processing device 402 also constituting machine-accessible storagemedia. The software 422 may further be transmitted or received over anetwork 420 via the network interface device 408.

While the machine-accessible storage medium 430 is shown in an exemplaryembodiment to be a single medium, the term “machine-accessible storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The term“machine-accessible storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present invention.The term “machine-accessible storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical andmagnetic media, etc.

Thus, some embodiments of an efficient string search have beendescribed. It is to be understood that the above description is intendedto be illustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method for filtering received content, themethod comprising: generating, by a hardware processor executinginstructions out of a memory, a model that corresponds to a digitalcontent file that is pre-classified under a first classification, thegenerated model including a number of conditions associated with thefirst classification that are related to statistical data regarding aset of keywords found in the digital content file; storing the model ina model repository in the memory; receiving a string of bytes over adata communication interface that is communicatively coupled to acommunication network; performing a string search on the received stringof bytes, wherein the string search generates statistical informationindicating whether the received string of bytes includes any keywordsfrom the set of keywords associated with at least the modelcorresponding to the first classification; comparing the generatedstatistical information with the model corresponding to the firstclassification; identifying that at least a portion of the receivedstring of bytes include a number of the keywords from the set ofkeywords associated with the model corresponding to the firstclassification; identifying whether the number of conditions associatedwith the first classification are satisfied based on the number ofkeywords identified in the received string of bytes that are from theset of keywords associated with the model; and processing the portion ofthe received string of bytes based on whether the number of conditionsare identified as being satisfied, wherein the portion is allowed to beprovided to a user device when the number of keywords does not satisfythe conditions associated with the first classification, and wherein theportion is not allowed to be provided to the user device when the numberof keywords does satisfy the conditions associated with the firstclassification.
 2. The method of claim 1, wherein the portion of thereceived string of bytes includes text in a non-delimited language. 3.The method of claim 2, wherein the non-delimited language is selectedfrom the group consisting of Chinese, Japanese, and Thai.
 4. The methodof claim 1, wherein each keyword corresponds to one or more N-grams. 5.The method of claim 4, further comprising identifying a plurality ofstates associated with each of the one or more N-grams.
 6. The method ofclaim 1, wherein the number of keywords associated with the firstclassification are part of one or more other models for classifying thedigital content.
 7. The method of claim 1, further comprising generatingone or more other models for classifying the digital content.
 8. Anon-transitory computer-readable storage medium having embodied thereona program executable by a hardware processor for performing a method forfiltering received content, the method comprising: generating, by thehardware processor, a model that corresponds to a digital content filethat is pre-classified under a first classification, the generated modelincluding a number of conditions associated with the firstclassification that are related to statistical data regarding a set ofkeywords found in the digital content file; storing the model in a modelrepository in memory; receiving a string of bytes over a datacommunication interface that is communicatively coupled to acommunication network; performing a string search on the received stringof bytes, wherein the string search generates statistical informationindicating whether the received string of bytes includes any keywordsfrom the set of keywords associated with the model corresponding to thefirst classification; comparing the generated statistical informationwith the model corresponding to the first classification; identifyingthat at least a portion of the received string of bytes include a numberof the keywords from the set of keywords associated with the modelcorresponding to the first classification; identifying whether thenumber of conditions associated with the first classification aresatisfied based on the number of keywords identified in the receivedstring of bytes that are from the set of keywords associated with themodel; and processing the portion of the received string of bytes basedon whether the number of conditions are identified as being satisfied,wherein the portion is allowed to be provided to a user device when thenumber of keywords does not satisfy the conditions associated with thefirst classification, and wherein the portion is not allowed to beprovided to the user device when the number of keywords does satisfy theconditions associated with the first classification.
 9. Thenon-transitory computer-readable storage medium of claim 8, wherein theportion of the received string of bytes includes text in a non-delimitedlanguage.
 10. The non-transitory computer-readable storage medium ofclaim 9, wherein the non-delimited language is selected from the groupconsisting of Chinese, Japanese, and Thai.
 11. The non-transitorycomputer-readable storage medium of claim 8, wherein each keywordcorresponds to one or more N-grams.
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein the programfurther comprises instructions executable to identify a plurality ofstates associated with each of the one or more N-grams.
 13. Thenon-transitory computer-readable storage medium of claim 8, wherein thenumber of keywords associated with the first classification are part ofone or more other models for classifying the digital content.
 14. Thenon-transitory computer-readable storage medium of claim 8, wherein theprogram further comprises instructions executable to generate one ormore other models for classifying the digital content.
 15. An apparatusfor performing a method for filtering received content, the apparatuscomprising: a memory; a model repository that stores a model thatcorresponds to a digital content file that is pre-classified under afirst classification, the model including a number of conditionsassociated with the first classification that are related to statisticaldata regarding a set of keywords found in the digital content file; acommunication interface that receives a string of bytes, thecommunication interface communicatively coupled to a communicationnetwork; and a hardware processor that executes instructions stored inthe memory, wherein execution of the instructions by the processor:performs a string search on the received string of bytes, wherein thestring search generates statistical information indicating whether thereceived string of bytes includes any keywords from the set of keywordsassociated with the model corresponding to the first classification;compares the generated statistical information with the modelcorresponding to the first classification; identifies that at least aportion of the received string of bytes include a number of the keywordsfrom the set of keywords associated with the model corresponding to thefirst classification; identifies whether the number of conditionsassociated with the first classification are satisfied based on thenumber of keywords identified in the received string of bytes as beingfrom the set of keywords associated with the model; and identifies thatthe portion of the received string of bytes corresponds to content thatis allowed to be provided to a user device based on identifying that thenumber of keywords does not satisfy the number of conditions associatedwith the first classification, wherein the portion of the receivedstring of bytes is identified as content that is not allowed to beprovided to the user device based on identifying that the number ofkeywords does satisfy the conditions associated with the firstclassification.
 16. The apparatus of claim 15, wherein the portion ofthe received string of bytes includes text in a non-delimited language.17. The apparatus of claim 16, wherein the non-delimited language isselected from the group consisting of Chinese, Japanese, and Thai. 18.The apparatus of claim 15, wherein each keyword corresponds to one ormore N-grams.
 19. The apparatus of claim 15, wherein the number ofkeywords associated with the first classification are part of one ormore other models for classifying the digital content.
 20. The apparatusof claim 15, wherein the processor executes further instructions togenerate one or more other models for classifying the digital content.